1
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Velpula VK, Sharma LD. Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion. Front Physiol 2023; 14:1175881. [PMID: 37383146 PMCID: PMC10293617 DOI: 10.3389/fphys.2023.1175881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/19/2023] [Indexed: 06/30/2023] Open
Abstract
Aim: To design an automated glaucoma detection system for early detection of glaucoma using fundus images. Background: Glaucoma is a serious eye problem that can cause vision loss and even permanent blindness. Early detection and prevention are crucial for effective treatment. Traditional diagnostic approaches are time consuming, manual, and often inaccurate, thus making automated glaucoma diagnosis necessary. Objective: To propose an automated glaucoma stage classification model using pre-trained deep convolutional neural network (CNN) models and classifier fusion. Methods: The proposed model utilized five pre-trained CNN models: ResNet50, AlexNet, VGG19, DenseNet-201, and Inception-ResNet-v2. The model was tested using four public datasets: ACRIMA, RIM-ONE, Harvard Dataverse (HVD), and Drishti. Classifier fusion was created to merge the decisions of all CNN models using the maximum voting-based approach. Results: The proposed model achieved an area under the curve of 1 and an accuracy of 99.57% for the ACRIMA dataset. The HVD dataset had an area under the curve of 0.97 and an accuracy of 85.43%. The accuracy rates for Drishti and RIM-ONE were 90.55 and 94.95%, respectively. The experimental results showed that the proposed model performed better than the state-of-the-art methods in classifying glaucoma in its early stages. Understanding the model output includes both attribution-based methods such as activations and gradient class activation map and perturbation-based methods such as locally interpretable model-agnostic explanations and occlusion sensitivity, which generate heatmaps of various sections of an image for model prediction. Conclusion: The proposed automated glaucoma stage classification model using pre-trained CNN models and classifier fusion is an effective method for the early detection of glaucoma. The results indicate high accuracy rates and superior performance compared to the existing methods.
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2
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Xu C, Chen Z, Zhang X, Peng Y, Tan Z, Fan Y, Liao X, Chen H, Shen J, Chen X. Accurate C/D ratio estimation with elliptical fitting for OCT image based on joint segmentation and detection network. Comput Biol Med 2023; 160:106903. [PMID: 37146494 DOI: 10.1016/j.compbiomed.2023.106903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/12/2023] [Accepted: 04/09/2023] [Indexed: 05/07/2023]
Abstract
Proper estimation of the cup-to-disc ratio (C/D ratio) plays a significant role in ophthalmic examinations, and it is urgent to improve the efficiency of C/D ratio automatic measurement. Therefore, we propose a new method for measuring the C/D ratio of OCTs in normal subjects. Firstly, the end-to-end deep convolution network is used to segment and detect the inner limiting membrane (ILM) and the two Bruch's membrane opening (BMO) terminations. Then, we introduce an ellipse fitting technique to post-process the edge of the optic disc. Finally, the proposed method is evaluated on 41 normal subjects using the optic-disc-area scanning mode of three machines: BV1000, Topcon 3D OCT-1, and Nidek ARK-1. In addition, pairwise correlation analyses are carried out to compare the C/D ratio measurement method of BV1000 to existing commercial OCT machines as well as other state-of-the-art methods. The correlation coefficient between the C/D ratio calculated by BV1000 and the C/D ratio calculated by manual annotation is 0.84, which indicates that the proposed method has a strong correlation with the results of manual annotation by ophthalmologists. Moreover, in comparison between BV1000, Topcon and Nidek in practical screening among normal subjects, the proportion of the C/D ratio less than 0.6 calculated by BV1000 accounts for 96.34%, which is the closest to the clinical statistics among the three OCT machines. The above experimental results and analysis show that the proposed method performs well in cup and disc detection and C/D ratio measurement, and compared with the existing commercial OCT equipment, the C/D ratio measurement results are relatively close to reality, which has certain clinical application value.
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Affiliation(s)
- Chenan Xu
- State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou, 215006, China
| | - Zhongyue Chen
- School of Electronics and Information Engineering and Medical Image Processing, Analysis and Visualization Lab, Soochow University, Suzhou, Jiangsu Province, 215006, China
| | - Xiao Zhang
- School of Electronics and Information Engineering and Medical Image Processing, Analysis and Visualization Lab, Soochow University, Suzhou, Jiangsu Province, 215006, China
| | - Yuanyuan Peng
- School of Electronics and Information Engineering and Medical Image Processing, Analysis and Visualization Lab, Soochow University, Suzhou, Jiangsu Province, 215006, China
| | - Zhiwei Tan
- School of Electronics and Information Engineering and Medical Image Processing, Analysis and Visualization Lab, Soochow University, Suzhou, Jiangsu Province, 215006, China
| | - Yu Fan
- Bigvision Medical Technology Co., Ltd., Suzhou, Jiangsu Province, 215006, China
| | - Xulong Liao
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong Province, 515041, China
| | - Haoyu Chen
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong Province, 515041, China
| | - Jiayan Shen
- School of Electronics and Information Engineering and Medical Image Processing, Analysis and Visualization Lab, Soochow University, Suzhou, Jiangsu Province, 215006, China
| | - Xinjian Chen
- State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou, 215006, China; School of Electronics and Information Engineering and Medical Image Processing, Analysis and Visualization Lab, Soochow University, Suzhou, Jiangsu Province, 215006, China.
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3
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Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State. Int J Mol Sci 2023; 24:ijms24032814. [PMID: 36769127 PMCID: PMC9917798 DOI: 10.3390/ijms24032814] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.
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Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ. Recent trends and advances in fundus image analysis: A review. Comput Biol Med 2022; 151:106277. [PMID: 36370579 DOI: 10.1016/j.compbiomed.2022.106277] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
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Affiliation(s)
- Shahzaib Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan; Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
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5
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Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding. INFORMATICS 2022. [DOI: 10.3390/informatics9020034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field.
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Gampala V, Maram B, Vigneshwari S, Cristin R. Glaucoma detection using hybrid architecture based on optimal deep neuro fuzzy network. INT J INTELL SYST 2022. [DOI: 10.1002/int.22845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Veerraju Gampala
- Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur Andhra Pradesh India
| | - Balajee Maram
- Department of Computer Science and Engineering GMR Institute of Technology Rajam Andhra Pradesh India
| | - S. Vigneshwari
- Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India
| | - R. Cristin
- Department of Computer Science and Engineering GMR Institute of Technology Rajam Andhra Pradesh India
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Latif J, Tu S, Xiao C, Ur Rehman S, Imran A, Latif Y. ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN APPLIED SCIENCES 2022. [DOI: 10.1007/s42452-022-04984-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
AbstractGlaucoma is one of the prevalent causes of blindness in the modern world. It is a salient chronic eye disease that leads to irreversible vision loss. The impediments of glaucoma can be restricted if it is identified at primary stages. In this paper, a novel two-phase Optic Disk localization and Glaucoma Diagnosis Network (ODGNet) has been proposed. In the first phase, a visual saliency map incorporated with shallow CNN is used for effective OD localization from the fundus images. In the second phase, the transfer learning-based pre-trained models are used for glaucoma diagnosis. The transfer learning-based models such as AlexNet, ResNet, and VGGNet incorporated with saliency maps are evaluated on five public retinal datasets (ORIGA, HRF, DRIONS-DB, DR-HAGIS, and RIM-ONE) to differentiate between normal and glaucomatous images. This study’s experimental results demonstrate that the proposed ODGNet evaluated on ORIGA for glaucoma diagnosis is the most predictive model and achieve 95.75, 94.90, 94.75, and 97.85% of accuracy, specificity, sensitivity, and area under the curve, respectively. These results indicate that the proposed OD localization method based on the saliency map and shallow CNN is robust, accurate and saves the computational cost.
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Singh LK, Khanna M, Pooja. A novel multimodality based dual fusion integrated approach for efficient and early prediction of glaucoma. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103468] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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9
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Singh LK, Pooja, Garg H, Khanna M. Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09426-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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10
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Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm. Med Biol Eng Comput 2022; 60:785-796. [PMID: 35080695 DOI: 10.1007/s11517-022-02510-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 01/18/2022] [Indexed: 10/19/2022]
Abstract
Glaucoma disease is optic neuropathy; in glaucoma, the optic nerve is damaged because the long duration of intraocular pressure can be caused blindness. Nowadays, deep learning classification algorithms are widely used to diagnose various diseases. However, in general, the training of deep learning algorithms is carried out by traditional gradient-based learning techniques that converge slowly and are highly likely to fall to the local minimum. In this study, we proposed a novel decision support system based on deep learning to diagnose glaucoma. The proposed system has two stages. In the first stage, the preprocessing of glaucoma disease data is performed by normalization and mean absolute deviation method, and in the second stage, the training of the deep learning is made by the artificial algae optimization algorithm. The proposed system is compared to traditional gradient-based deep learning and deep learning trained with other optimization algorithms like genetic algorithm, particle swarm optimization, bat algorithm, salp swarm algorithm, and equilibrium optimizer. Furthermore, the proposed system is compared to the state-of-the-art algorithms proposed for the glaucoma detection. The proposed system has outperformed other algorithms in terms of classification accuracy, recall, precision, false positive rate, and F1-measure by 0.9815, 0.9795, 0.9835, 0.0165, and 0.9815, respectively.
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11
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Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph181910003. [PMID: 34639303 PMCID: PMC8508413 DOI: 10.3390/ijerph181910003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/12/2021] [Accepted: 09/17/2021] [Indexed: 01/21/2023]
Abstract
Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.
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12
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Omar Y, ElSheikh MAE, Hodhod R. GLAUDIA: A predicative system for glaucoma diagnosis in mass scanning. Health Informatics J 2021; 27:14604582211009276. [PMID: 33913369 DOI: 10.1177/14604582211009276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Glaucoma is a serious eye disease characterized by dysfunction and loss of retinal ganglion cells (RGCs) which can eventually lead to loss of vision. Robust mass screening may help to extend the symptom-free life for the affected patients. The retinal optic nerve fiber layer can be assessed using optical coherence tomography, scanning laser polarimetry (SLP), and Heidelberg Retina Tomography (HRT) scanning methods which, unfortunately, are expensive methods and hence, a novel automated glaucoma diagnosis system is needed. This paper proposes a new model for mass screening that aims to decrease the false negative rate (FNR). The model is based on applying nine different machine learning techniques in a majority voting model. The top five techniques that provide the highest accuracy will be used to build a consensus ensemble to make the final decision. The results from applying both models on a dataset with 499 records show a decrease in the accuracy rate from 90% to 83% and a decrease in false negative rate (FNR) from 8% to 0% for majority voting and consensus model, respectively. These results indicate that the proposed model can reduce FNR dramatically while maintaining a reasonable overall accuracy which makes it suitable for mass screening.
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Affiliation(s)
- Yasser Omar
- Arab Academy for Science, Technology, and Maritime Transport, Egypt
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13
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ECNet: An evolutionary convolutional network for automated glaucoma detection using fundus images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102559] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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14
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Xu X, Guan Y, Li J, Ma Z, Zhang L, Li L. Automatic glaucoma detection based on transfer induced attention network. Biomed Eng Online 2021; 20:39. [PMID: 33892734 PMCID: PMC8066979 DOI: 10.1186/s12938-021-00877-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/13/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Glaucoma is one of the causes that leads to irreversible vision loss. Automatic glaucoma detection based on fundus images has been widely studied in recent years. However, existing methods mainly depend on a considerable amount of labeled data to train the model, which is a serious constraint for real-world glaucoma detection. METHODS In this paper, we introduce a transfer learning technique that leverages the fundus feature learned from similar ophthalmic data to facilitate diagnosing glaucoma. Specifically, a Transfer Induced Attention Network (TIA-Net) for automatic glaucoma detection is proposed, which extracts the discriminative features that fully characterize the glaucoma-related deep patterns under limited supervision. By integrating the channel-wise attention and maximum mean discrepancy, our proposed method can achieve a smooth transition between general and specific features, thus enhancing the feature transferability. RESULTS To delimit the boundary between general and specific features precisely, we first investigate how many layers should be transferred during training with the source dataset network. Next, we compare our proposed model to previously mentioned methods and analyze their performance. Finally, with the advantages of the model design, we provide a transparent and interpretable transferring visualization by highlighting the key specific features in each fundus image. We evaluate the effectiveness of TIA-Net on two real clinical datasets and achieve an accuracy of 85.7%/76.6%, sensitivity of 84.9%/75.3%, specificity of 86.9%/77.2%, and AUC of 0.929 and 0.835, far better than other state-of-the-art methods. CONCLUSION Different from previous studies applied classic CNN models to transfer features from the non-medical dataset, we leverage knowledge from the similar ophthalmic dataset and propose an attention-based deep transfer learning model for the glaucoma diagnosis task. Extensive experiments on two real clinical datasets show that our TIA-Net outperforms other state-of-the-art methods, and meanwhile, it has certain medical value and significance for the early diagnosis of other medical tasks.
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Affiliation(s)
- Xi Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Yu Guan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Zerui Ma
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Li Zhang
- Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Li Li
- Beijing Children’s Hospital, Capital Medical University, Beijing, China
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15
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Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.05.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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16
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Chaudhary PK, Pachori RB. Automatic diagnosis of glaucoma using two-dimensional Fourier-Bessel series expansion based empirical wavelet transform. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102237] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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17
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Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.04.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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18
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Kishore B, Ananthamoorthy NP. Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier. Genomics 2020; 112:3089-3096. [PMID: 32470644 DOI: 10.1016/j.ygeno.2020.05.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 05/19/2020] [Indexed: 10/24/2022]
Abstract
Automatic classification of glaucoma from fundus images is a vital diagnostic tool for Computer-Aided Diagnosis System (CAD). In this work, a novel fused feature extraction technique and ensemble classifier fusion is proposed for diagnosis of glaucoma. The proposed method comprises of three stages. Initially, the fundus images are subjected to preprocessing followed by feature extraction and feature fusion by Intra-Class and Extra-Class Discriminative Correlation Analysis (IEDCA). The feature fusion approach eliminates between-class correlation while retaining sufficient Feature Dimension (FD) for Correlation Analysis (CA). The fused features are then fed to the classifiers namely Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) for classification individually. Finally, Classifier fusion is also designed which combines the decision of the ensemble of classifiers based on Consensus-based Combining Method (CCM). CCM based Classifier fusion adjusts the weights iteratively after comparing the outputs of all the classifiers. The proposed fusion classifier provides a better improvement in accuracy and convergence when compared to the individual algorithms. A classification accuracy of 99.2% is accomplished by the two-level hybrid fusion approach. The method is evaluated on the public datasets High Resolution Fundus (HRF) and DRIVE datasets with cross dataset validation.
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19
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Wang J, Shao W, Kim J. Combining MF-DFA and LSSVM for retina images classification. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101943] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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20
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Gudigar A, Raghavendra U, Hegde A, Kalyani M, Ciaccio EJ, Rajendra Acharya U. Brain pathology identification using computer aided diagnostic tool: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105205. [PMID: 31786457 DOI: 10.1016/j.cmpb.2019.105205] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 11/12/2019] [Accepted: 11/12/2019] [Indexed: 05/28/2023]
Abstract
Computer aided diagnostic (CAD) has become a significant tool in expanding patient quality-of-life by reducing human errors in diagnosis. CAD can expedite decision-making on complex clinical data automatically. Since brain diseases can be fatal, rapid identification of brain pathology to prolong patient life is an important research topic. Many algorithms have been proposed for efficient brain pathology identification (BPI) over the past decade. Constant refinement of the various image processing algorithms must take place to expand performance of the automatic BPI task. In this paper, a systematic survey of contemporary BPI algorithms using brain magnetic resonance imaging (MRI) is presented. A summarization of recent literature provides investigators with a helpful synopsis of the domain. Furthermore, to enhance the performance of BPI, future research directions are indicated.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
| | - Ajay Hegde
- Neurosurgery, Institute of Neurological Sciences, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom
| | - M Kalyani
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, United States
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599491, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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21
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Vargas-Lopez O, Amezquita-Sanchez JP, De-Santiago-Perez JJ, Rivera-Guillen JR, Valtierra-Rodriguez M, Toledano-Ayala M, Perez-Ramirez CA. A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection. SENSORS (BASEL, SWITZERLAND) 2019; 20:E9. [PMID: 31861320 PMCID: PMC6983035 DOI: 10.3390/s20010009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 12/06/2019] [Accepted: 12/11/2019] [Indexed: 02/01/2023]
Abstract
Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.
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Affiliation(s)
- Olivia Vargas-Lopez
- ENAP RG, Department of Biomedical Engineering, Faculty of Engineering, Autonomous University of Queretaro, Queretaro 76144, Mexico; (O.V.-L.); (J.P.A.-S.)
| | - Juan P. Amezquita-Sanchez
- ENAP RG, Department of Biomedical Engineering, Faculty of Engineering, Autonomous University of Queretaro, Queretaro 76144, Mexico; (O.V.-L.); (J.P.A.-S.)
- ENAP RG, Department of Electromechanical Engineering, Faculty of Engineering, Autonomous University of Queretaro, San Juan del Rio, Queretaro 76807, Mexico; (J.J.D.-S.-P.); (J.R.R.-G.); (M.V.-R.)
| | - J. Jesus De-Santiago-Perez
- ENAP RG, Department of Electromechanical Engineering, Faculty of Engineering, Autonomous University of Queretaro, San Juan del Rio, Queretaro 76807, Mexico; (J.J.D.-S.-P.); (J.R.R.-G.); (M.V.-R.)
| | - Jesus R. Rivera-Guillen
- ENAP RG, Department of Electromechanical Engineering, Faculty of Engineering, Autonomous University of Queretaro, San Juan del Rio, Queretaro 76807, Mexico; (J.J.D.-S.-P.); (J.R.R.-G.); (M.V.-R.)
| | - Martin Valtierra-Rodriguez
- ENAP RG, Department of Electromechanical Engineering, Faculty of Engineering, Autonomous University of Queretaro, San Juan del Rio, Queretaro 76807, Mexico; (J.J.D.-S.-P.); (J.R.R.-G.); (M.V.-R.)
| | | | - Carlos A. Perez-Ramirez
- ENAP RG, Department of Biomedical Engineering, Faculty of Engineering, Autonomous University of Queretaro, Queretaro 76144, Mexico; (O.V.-L.); (J.P.A.-S.)
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Benzebouchi NE, Azizi N, Ashour AS, Dey N, Sherratt RS. Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis. J EXP THEOR ARTIF IN 2019. [DOI: 10.1080/0952813x.2019.1653383] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Nacer Eddine Benzebouchi
- Computer Science Department, Labged Laboratory, Badji Mokhtar Annaba University, Annaba, Algeria
| | - Nabiha Azizi
- Computer Science Department, Labged Laboratory, Badji Mokhtar Annaba University, Annaba, Algeria
| | - Amira S. Ashour
- Department of Electronics Engineering and Communication Engineering, Tanta University, Tanta, Egypt
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, Kolkata, India
| | - R. Simon Sherratt
- Department of Biomedical Engineering, University of Reading, Reading, UK
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23
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Raghavendra U, Gudigar A, Bhandary SV, Rao TN, Ciaccio EJ, Acharya UR. A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images. J Med Syst 2019; 43:299. [DOI: 10.1007/s10916-019-1427-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 07/21/2019] [Indexed: 12/12/2022]
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24
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George N, Jiji C. Two stage contour evolution for automatic segmentation of choroid and cornea in OCT images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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25
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SHARMA RAHUL, SIRCAR PRADIP, PACHORI RB, BHANDARY SULATHAV, ACHARYA URAJENDRA. AUTOMATED GLAUCOMA DETECTION USING CENTER SLICE OF HIGHER ORDER STATISTICS. J MECH MED BIOL 2019. [DOI: 10.1142/s0219519419400116] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Glaucoma is one of the leading causes of blindness. The raised intraocular pressure is one of the important modifiable risk factor causing glaucomatous optic nerve damage. Glaucomatous optic nerve damage is seen as increase in the cupping of the optic disc and loss of neuroretinal rim. An automated detection system using nonlinear higher order statistics (HOS) based method is used to capture the detailed information present in the fundus image efficiently. The center slice of bispectrum and bicepstrum are applied on fundus images. Various features are extracted from the diagonal of these central slices. In order to reduce the number of features the locality sensitive discriminant analysis (LSDA) data reduction technique method is implemented. The ranked LSDA features are fed to support vector machine (SVM) classifier with various kernels for automated glaucoma detection. The simulation is performed on two databases. The proposed algorithm has yielded classification accuracy of 98.8% and 95% using entire private and public databases, respectively. The proposed technique achieved the highest classification accuracy, hence, confirm the diagnosis of ophthalmologists and can be employed in the community health care centers and hospitals.
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Affiliation(s)
- RAHUL SHARMA
- Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - PRADIP SIRCAR
- Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
| | - R. B. PACHORI
- Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
| | - SULATHA V. BHANDARY
- Department of Ophthalmology, Kasturba Medical College, Manipal 576104, India
| | - U. RAJENDRA ACHARYA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
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26
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Maheshwari S, Kanhangad V, Pachori RB, Bhandary SV, Acharya UR. Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Comput Biol Med 2019; 105:72-80. [DOI: 10.1016/j.compbiomed.2018.11.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/18/2022]
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